Kubernetes-Based Model Serving on EKS for Staging.

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Pulumi · Accepted Answer

Creating a Kubernetes-based model serving environment on Amazon EKS (Elastic Kubernetes Service) involves several steps, including setting up an EKS cluster, deploying Kubernetes resources, and ensuring proper configuration for staging purposes. Pulumi allows you to define this infrastructure as code using Python, which makes it reproducible, versionable, and easy to maintain.

First, you'll need to create an EKS cluster. For staging environments, it's often a good practice to set up resource tagging for tracking purposes and enable logging to monitor the cluster's behavior.

In this guide, I'll walk you through the process of creating an EKS cluster using the Pulumi EKS package, which simplifies a lot of the lower-level details, and setting up a basic Kubernetes deployment and service for model serving. This assumes that you have the necessary permissions to create and manage AWS and EKS resources.

Below is the Pulumi program in Python that sets up such an environment:

```python
import pulumi
import pulumi_eks as eks

# Create an EKS cluster with default settings.
# This will automatically create the necessary IAM roles and VPC resources.
# Note that you can customize the settings as needed for your staging environment.
cluster = eks.Cluster('staging-eks-cluster',
                      tags={
                          'Environment': 'Staging',
                          'Project': 'ModelServing'
                      })

# Once the cluster is created, we can obtain a kubeconfig that can be used to
# communicate with the Kubernetes cluster by other services or for manual administration.
kubeconfig = cluster.kubeconfig.apply(lambda c: c.raw)

# Example: Defining a simple Kubernetes deployment for model serving.
# In a real-world scenario, you would replace the image with your model serving image,
# configure the necessary environment variables, resources, and any other required properties.
app_labels = {'app': 'model-serving'}
deployment = eks.Deployment('model-serving-deployment',
                            spec={
                                'selector': {'matchLabels': app_labels},
                                'replicas': 2,
                                'template': {
                                    'metadata': {'labels': app_labels},
                                    'spec': {
                                        'containers': [{
                                            'name': 'model-serving-container',
                                            'image': 'your-model-serving-image:latest'
                                        }]
                                    }
                                }
                            },
                            opts=pulumi.ResourceOptions(provider=cluster.provider))

# Example: Creating a Kubernetes service to expose the model serving deployment.
# The type of service (e.g., LoadBalancer, NodePort) will depend on how you wish to expose your service
# within your network or publicly for staging.
service = eks.Service('model-serving-service',
                      spec={
                          'selector': app_labels,
                          'ports': [{'port': 80, 'targetPort': 'http'}],
                          'type': 'LoadBalancer'
                      },
                      opts=pulumi.ResourceOptions(provider=cluster.provider))

# Export the kubeconfig to allow easy access to our cluster from our local machine
# This is useful for manual debugging, administration, etc.
pulumi.export('kubeconfig', kubeconfig)

# (Optional) Export the public URL of the LoadBalancer service
# This would be the URL you use to interact with the model serving API, for example.
public_url = service.status.apply(lambda s: s['load_balancer']['ingress'][0]['hostname'])
pulumi.export('public_url', public_url)
```

In the example above, we create an EKS cluster with standard settings. We tag the cluster with 'Environment' and 'Project' tags to signify its purpose and ownership. Then, we define a Kubernetes deployment with a placeholder image `your-model-serving-image:latest`. In your case, you would replace this with the actual image that contains your model serving application. We also create a service of type `LoadBalancer`, which would give us a public endpoint to access the application.

We export the `kubeconfig` for potential use in CI/CD pipelines or local kubectl usage and the `public_url` for the newly created LoadBalancer service, so you know how to access your model serving application.

It's important to note that this example assumes you have an image ready for deployment which contains your model serving code. In a real-world scenario, you would also configure other resources such as data storage, security groups, and potentially a CI/CD pipeline for deploying your application changes to the staging environment.